List of matrices (or data frames), one matrix per study. Each matrix has one row per gene and one column per replicate and gives the expression data for both conditions with the order specified in the classes argument. All studies must have the same genes.
If the data are already stored as ExpressionSets objects (cf. Bioconductor project), then exprs(yourdata) will give an appropriate element of the list esets used for this function.
classes
List of class memberships, one per study. Each vector or factor of the list can only contain two levels which correspond to the two conditions studied.
moderated
Method to calculate the test statistic inside each study from which the effect size is computed. moderated has to be chosen between "limma", "SMVar" and "t".
BHth
Benjamini Hochberg threshold. By default, the False Discovery Rate is controlled at 5%.
Value
List
Study1
Vector of indices of differentially expressed genes in study 1. Similar names are given for the other individual studies.
AllIndStudies
Vector of indices of differentially expressed genes found by at least one of the individual studies.
Meta
Vector of indices of differentially expressed genes in the meta-analysis.
TestStatistic
Vector with test statistics for differential expression in the meta-analysis.
Note
While the invisible object resulting from this function contains
the values described previously, other quantities of interest are printed:
DE,IDD,Loss,IDR,IRR.
All these quantities are defined in function IDDIDR and in (Marot et al., 2009)
Author(s)
Guillemette Marot
References
Marot, G., Foulley, J.-L., Mayer, C.-D., Jaffrezic, F. (2009) Moderated effect size and p-value combinations for microarray meta-analyses. Bioinformatics. 25 (20): 2692-2699.
Examples
data(Singhdata)
#Meta-analysis
res=EScombination(esets=Singhdata$esets,classes=Singhdata$classes)
#Number of differentially expressed genes in the meta-analysis
length(res$Meta)
#To plot an histogram of raw p-values
rawpval=2*(1-pnorm(abs(res$TestStatistic)))
hist(rawpval,nclass=100)